Building voice agents taught me a simple truth: users forgive imperfect answers faster than they forgive awkward silence. In chat, latency is annoying. In voice, latency breaks trust.
In my recent prototypes, the biggest improvements did not come from swapping bigger models. They came from orchestration decisions that reduce the gap between "user finishes speaking" and "agent begins responding."
What Matters Most
- Start reasoning while speech is still streaming, not after the final transcript.
- Keep planning loops short: detect intent first, elaborate second.
- Use fast TTS that supports interruption, because real users interrupt naturally.
- Store compact context summaries instead of long transcript dumps.
What I Am Testing Next
My next step is a hybrid approach: deterministic routing for predictable tasks and LLM policy only where ambiguity is high. The target is a voice agent that feels calm, responsive, and consistent over long sessions.